There are certain factors that define the quality of data as usefulness in accuracy, consistency; completeness etc. data must be qualified to be of usefulness. Thus data must satisfy the intended purpose.
The seven stages of data mining process are as below-
Some strategies that can be included in data reduction is reducing the number of attributes, replacing original data by smaller filtered forms and compression of data.
The big data revolution has given birth to different kinds, types and stages of data analysis. The key is to gain the right information which delivers knowledge that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes.
Descriptive analysis- This is the most basic form of analytics. It tells the retriever what actually has happened. This model analysis the data in real time and older data for the insight of how to react in the future, to find reasons of success of failure in the past. Majority of big data analytics use descriptive model of data analytics.
Predictive Analytics- Predictive analysis is a step of data reduction. Analyzing past data patterns and trends can accurately inform a business about what could happen in the future. This helps in setting realistic goals for the business, effective planning and restraining expectations. It tells us the about what can happen in the future based on past incidents. Organizations collect contextual data and relate it with other customer user behavior datasets and web server data to get real insights through predictive analytics.
Prescriptive Analytics- Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximize key business metrics. It tells a business what should it do? It is based on optimization to achieve best outcomes and how to achieve them and uncertainty identification to make better decisions.
Shahu, H., Sharma, S. & Gondhalakar, S. (n.d.). A Brief Overview on Data Mining Survey. International Journal of Computer Technology and Electronics Engineering 1(3).
Sivaraja, U., Kamal, M.M., Irani,Z. & Verrakkody, V. Critical analysis of big data challenges and analytical methods. Journal of Business Research 70.
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